Method and system to provide cost of lost opportunity to operators in real time using advance process control

ABSTRACT

A field device, method, and non-transitory computer readable medium provide for cost of lost opportunity to operators in real-time using an advance process control. The field device includes a memory and a processor operably connected to the memory. The processor receives current values and average values for controlled variables and manipulated variables; determines costs of lost opportunity for each of controlled variable variance issues, limit issues, model quality issues, inferential quality issues, and variable model issues based on the current values and the average values of the controlled variables; and stores the costs of lost opportunity for the field device.

TECHNICAL FIELD

This disclosure relates generally to advance process control (APC)sensors. More specifically, this disclosure relates to methods andsystems to provide cost of lost opportunity to operators in real timeusing advance process control.

BACKGROUND

Advance process control (APC) is used in the process industry to drivecomplex systems that are interactive with transport delay to operate atlimits and deliver operational performance and economic benefits tocustomers. Sustaining the performance of the controllers is importantfor realizing the benefits that these applications promise. Theperformance of an APC application deteriorates over time due toequipment degradation, changes in the operations of the process and ofthe controller, constrained APC limits, variables dropped from APC andvarious other reasons.

SUMMARY

This disclosure provides for determining cost of lost opportunity tooperators in real-time using an advance process control.

In a first embodiment, a field device including a memory and a processoroperably connected to the memory is provided. The processor receivescurrent values and average values for controlled variables andmanipulated variables; determines costs of lost opportunity for each ofcontrolled variable variance issues, limit issues, model quality issues,inferential quality issues, and variable model issues based on thecurrent values and the average values of the controlled variables; andstores the costs of lost opportunity for the field device.

In a second embodiment, a method for a field device is provided. Thesystem includes receiving current values and average values forcontrolled variables and manipulated variables; determining costs oflost opportunity for each of controlled variable variance issues, limitissues, model quality issues, inferential quality issues, and variablemodel issues based on the current values and the average values of thecontrolled variables; and storing the costs of lost opportunity for thefield device.

In a third embodiment, a non-transitory computer readable medium isprovided. The computer readable medium machine-readable medium isencoded with executable instructions that, when executed, cause one ormore processors to receive current values and average values forcontrolled variables and manipulated variables; determine costs of lostopportunity for each of controlled variable variance issues, limitissues, model quality issues, inferential quality issues, and variablemodel issues based on the current values and the average values of thecontrolled variables; and store the costs of lost opportunity for thefield device.

Other technical features may be readily apparent to one skilled in theart from the following figures, descriptions, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following description, taken in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates an example industrial control and automation systemaccording to this disclosure;

FIGS. 2A and 2B illustrate an exemplary interface for providing cost oflost opportunity to operators in real time using advance process controlaccording to this disclosure; and

FIG. 3 illustrates an example method for providing cost of lostopportunity to operators in real time using advance process controlaccording to this disclosure.

DETAILED DESCRIPTION

FIGS. 1 through 3, discussed below, and the various embodiments used todescribe the principles of the present disclosure in this patentdocument are by way of illustration only and should not be construed inany way to limit the scope of the disclosure. Those skilled in the artwill understand that the principles of the present disclosure may beimplemented in any type of suitably arranged device or system.

Embodiments of this disclosure provide benefits of limiting issues in anAPC. The APC calculates the cost of lost opportunity due to incorrect,over constrained, or equipment size limits of manipulated variables(MVs) and/or controlled variables (CVs).

The disclosed embodiments provide benefits of increasing model quality.The APC calculates the lost opportunity due to model quality issues. Theprocess is nonlinear and time variant, while the models of APC aretypically linear time invariant (LTI). The controller dynamic responseand embedded optimizer solution gets affected when the process driftsdue to change in feed quality, operating point, ambient condition andother operating conditions.

The disclosed embodiments provide benefits of increasing inferentialquality. The APC calculates the lost opportunity due to inferentialquality issues. Soft sensors or inferred properties are quite frequentlyused as controlled variables in APCs as proxies to the lab or analyzervalues. Many of these inferred properties limits or qualityspecifications are frequently constraints to the optimization.Therefore, the quality of the inferential has direct impact on thebenefits that can be achieved by APCs.

The disclosed embodiments provide benefits of monitoring droppedvariable modes. The APC calculates the cost of lost opportunity whensome of the variables are excluded or dropped from the APC. Due tovarious transient issues, operators may remove some of the variablesfrom consideration of the controller. In some embodiments, the variablesmay be removed from the controller for an extended period of time. Thecontroller operation in such conditions becomes sub optimal.

The disclosed embodiments provide benefits of optimizationconfiguration. In many APC applications, the costs of variables foroptimization or objective coefficients do not represent real costs.These costs are entered to give just a direction to the variables. Evenwhen costs entered are real, they may not be updated with the changes inmarket value or changes in mode of operation. This may result in anoptimizer not targeting the true optimum value.

The disclosed embodiments provide benefits of reduced noise and varianceof controlled variables. The noise or variance in controlled variablescan be a result of measurement noise, underlying MV PID controlleroscillations or can be due to high variance in measured or unmeasureddisturbance variables. In some embodiments, high variance can also bedue to mismatch between controller model and process and when the APC istuned aggressively. Mismatch may also be due to nonlinearity that mayresult in limit cycling of the controller. One of the impacts of highnoise and variance is that the APC pushes the average value ofcontrolled variables away from the limits in order to maintain the CVswithin limits. This causes loss of opportunity.

FIG. 1 illustrates an example industrial process control and automationsystem 100 according to this disclosure. As shown in FIG. 1, the system100 includes various components that facilitate production or processingof at least one product or other material. For instance, the system 100is used here to facilitate control over components in one or multipleplants 101 a-101 n. Each plant 101 a-101 n represents one or moreprocessing facilities (or one or more portions thereof), such as one ormore manufacturing facilities for producing at least one product orother material. In general, each plant 101 a-101 n may implement one ormore processes and can individually or collectively be referred to as aprocess system. A process system generally represents any system orportion thereof configured to process one or more products or othermaterials in some manner.

In FIG. 1, the system 100 is implemented using the Purdue model ofprocess control. In the Purdue model, “Level 0” may include one or moresensors 102 a and one or more actuators 102 b. The sensors 102 a andactuators 102 b represent components in a process system that mayperform any of a wide variety of functions. For example, the sensors 102a could measure a wide variety of characteristics in the process system,such as temperature, pressure, flow rate, or a voltage transmittedthrough a cable. Also, the actuators 102 b could alter a wide variety ofcharacteristics in the process system. The sensors 102 a and actuators102 b could represent any other or additional components in any suitableprocess system. Each of the sensors 102 a includes any suitablestructure for measuring one or more characteristics in a process system.Each of the actuators 102 b includes any suitable structure foroperating on or affecting one or more conditions in a process system.

At least one network 104 is coupled to the sensors 102 a and actuators102 b. The network 104 facilitates interaction with the sensors 102 aand actuators 102 b. For example, the network 104 could transportmeasurement data from the sensors 102 a and provide control signals tothe actuators 102 b. The network 104 could represent any suitablenetwork or combination of networks. As particular examples, the network104 could represent an Ethernet network, an electrical signal network(such as a HART or FOUNDATION FIELDBUS (FF) network), a pneumaticcontrol signal network, or any other or additional type(s) ofnetwork(s).

In the Purdue model, “Level 1” may include one or more controllers 106,which are coupled to the network 104. Among other things, eachcontroller 106 may use the measurements from one or more sensors 102 ato control the operation of one or more actuators 102 b. For example, acontroller 106 could receive measurement data from one or more sensors102 a and use the measurement data to generate control signals for oneor more actuators 102 b. Multiple controllers 106 could also operate inredundant configurations, such as when one controller 106 operates as aprimary controller while another controller 106 operates as a backupcontroller (which synchronizes with the primary controller and can takeover for the primary controller in the event of a fault with the primarycontroller). Each controller 106 includes any suitable structure forinteracting with one or more sensors 102 a and controlling one or moreactuators 102 b. Each controller 106 could, for example, represent amultivariable controller, such as a Robust Multivariable PredictiveControl Technology (RMPCT) controller or other type of controllerimplementing model predictive control (MPC) or other advanced predictivecontrol (APC). As a particular example, each controller 106 couldrepresent a computing device running a real-time operating system.

Two networks 108 are coupled to the controllers 106. The networks 108facilitate interaction with the controllers 106, such as by transportingdata to and from the controllers 106. The networks 108 could representany suitable networks or combination of networks. As particularexamples, the networks 108 could represent a pair of Ethernet networksor a redundant pair of Ethernet networks, such as a FAULT TOLERANTETHERNET (FTE) network from HONEYWELL INTERNATIONAL INC.

At least one switch/firewall 110 couples the networks 108 to twonetworks 112. The switch/firewall 110 may transport traffic from onenetwork to another. The switch/firewall 110 may also block traffic onone network from reaching another network. The switch/firewall 110includes any suitable structure for providing communication betweennetworks, such as a HONEYWELL CONTROL FIREWALL (CF9) device. Thenetworks 112 could represent any suitable networks, such as a pair ofEthernet networks or an FTE network.

In the Purdue model, “Level 2” may include one or more machine-levelcontrollers 114 coupled to the networks 112. The machine-levelcontrollers 114 perform various functions to support the operation andcontrol of the controllers 106, sensors 102 a, and actuators 102 b,which could be associated with a particular piece of industrialequipment (such as a boiler or other machine). For example, themachine-level controllers 114 could log information collected orgenerated by the controllers 106, such as measurement data from thesensors 102 a or control signals for the actuators 102 b. Themachine-level controllers 114 could also execute applications thatcontrol the operation of the controllers 106, thereby controlling theoperation of the actuators 102 b. In addition, the machine-levelcontrollers 114 could provide secure access to the controllers 106. Eachof the machine-level controllers 114 includes any suitable structure forproviding access to, control of, or operations related to a machine orother individual piece of equipment. Each of the machine-levelcontrollers 114 could, for example, represent a server computing devicerunning a MICROSOFT WINDOWS operating system. Although not shown,different machine-level controllers 114 could be used to controldifferent pieces of equipment in a process system (where each piece ofequipment is associated with one or more controllers 106, sensors 102 a,and actuators 102 b).

One or more operator stations 116 are coupled to the networks 112. Theoperator stations 116 represent computing or communication devicesproviding user access to the machine-level controllers 114, which couldthen provide user access to the controllers 106 (and possibly thesensors 102 a and actuators 102 b). As particular examples, the operatorstations 116 could allow users to review the operational history of thesensors 102 a and actuators 102 b using information collected by thecontrollers 106 and/or the machine-level controllers 114. The operatorstations 116 could also allow the users to adjust the operation of thesensors 102 a, actuators 102 b, controllers 106, or machine-levelcontrollers 114. In addition, the operator stations 116 could receiveand display warnings, alerts, or other messages or displays generated bythe controllers 106 or the machine-level controllers 114. Each of theoperator stations 116 includes any suitable structure for supportinguser access and control of one or more components in the system 100.Each of the operator stations 116 could, for example, represent acomputing device running a MICROSOFT WINDOWS operating system.

At least one router/firewall 118 couples the networks 112 to twonetworks 120. The router/firewall 118 includes any suitable structurefor providing communication between networks, such as a secure router orcombination router/firewall. The networks 120 could represent anysuitable networks, such as a pair of Ethernet networks or an FTEnetwork.

In the Purdue model, “Level 3” may include one or more unit-levelcontrollers 122 coupled to the networks 120. Each unit-level controller122 is typically associated with a unit in a process system, whichrepresents a collection of different machines operating together toimplement at least part of a process. The unit-level controllers 122perform various functions to support the operation and control ofcomponents in the lower levels. For example, the unit-level controllers122 could log information collected or generated by the components inthe lower levels, execute applications that control the components inthe lower levels, and provide secure access to the components in thelower levels. Each of the unit-level controllers 122 includes anysuitable structure for providing access to, control of, or operationsrelated to one or more machines or other pieces of equipment in aprocess unit. Each of the unit-level controllers 122 could, for example,represent a server computing device running a MICROSOFT WINDOWSoperating system. Although not shown, different unit-level controllers122 could be used to control different units in a process system (whereeach unit is associated with one or more machine-level controllers 114,controllers 106, sensors 102 a, and actuators 102 b).

Access to the unit-level controllers 122 may be provided by one or moreoperator stations 124. Each of the operator stations 124 includes anysuitable structure for supporting user access and control of one or morecomponents in the system 100. Each of the operator stations 124 could,for example, represent a computing device running a MICROSOFT WINDOWSoperating system.

At least one router/firewall 126 couples the networks 120 to twonetworks 128. The router/firewall 126 includes any suitable structurefor providing communication between networks, such as a secure router orcombination router/firewall. The networks 128 could represent anysuitable networks, such as a pair of Ethernet networks or an FTEnetwork.

In the Purdue model, “Level 4” may include one or more plant-levelcontrollers 130 coupled to the networks 128. Each plant-level controller130 is typically associated with one of the plants 101 a-101 n, whichmay include one or more process units that implement the same, similar,or different processes. The plant-level controllers 130 perform variousfunctions to support the operation and control of components in thelower levels. As particular examples, the plant-level controller 130could execute one or more manufacturing execution system (MES)applications, scheduling applications, or other or additional plant orprocess control applications. Each of the plant-level controllers 130includes any suitable structure for providing access to, control of, oroperations related to one or more process units in a process plant. Eachof the plant-level controllers 130 could, for example, represent aserver computing device running a MICROSOFT WINDOWS operating system.

Access to the plant-level controllers 130 may be provided by one or moreoperator stations 132. Each of the operator stations 132 includes anysuitable structure for supporting user access and control of one or morecomponents in the system 100. Each of the operator stations 132 could,for example, represent a computing device running a MICROSOFT WINDOWSoperating system.

At least one router/firewall 134 couples the networks 128 to one or morenetworks 136. The router/firewall 134 includes any suitable structurefor providing communication between networks, such as a secure router orcombination router/firewall. The network 136 could represent anysuitable network, such as an enterprise-wide Ethernet or other networkor all or a portion of a larger network (such as the Internet).

In the Purdue model, “Level 5” may include one or more enterprise-levelcontrollers 138 coupled to the network 136. Each enterprise-levelcontroller 138 is typically able to perform planning operations formultiple plants 101 a-101 n and to control various aspects of the plants101 a-101 n. The enterprise-level controllers 138 can also performvarious functions to support the operation and control of components inthe plants 101 a-101 n. As particular examples, the enterprise-levelcontroller 138 could execute one or more order processing applications,enterprise resource planning (ERP) applications, advanced planning andscheduling (APS) applications, or any other or additional enterprisecontrol applications. Each of the enterprise-level controllers 138includes any suitable structure for providing access to, control of, oroperations related to the control of one or more plants. Each of theenterprise-level controllers 138 could, for example, represent a servercomputing device running a MICROSOFT WINDOWS operating system. In thisdocument, the term “enterprise” refers to an organization having one ormore plants or other processing facilities to be managed. Note that if asingle plant 101 a is to be managed, the functionality of theenterprise-level controller 138 could be incorporated into theplant-level controller 130.

Access to the enterprise-level controllers 138 may be provided by one ormore operator stations 140. Each of the operator stations 140 includesany suitable structure for supporting user access and control of one ormore components in the system 100. Each of the operator stations 140could, for example, represent a computing device running a MICROSOFTWINDOWS operating system.

Various levels of the Purdue model can include other components, such asone or more databases. The database(s) associated with each level couldstore any suitable information associated with that level or one or moreother levels of the system 100. For example, a historian 141 can becoupled to the network 136. The historian 141 could represent acomponent that stores various information about the system 100. Thehistorian 141 could, for instance, store information used duringproduction scheduling and optimization. The historian 141 represents anysuitable structure for storing and facilitating retrieval ofinformation. Although shown as a single centralized component coupled tothe network 136, the historian 141 could be located elsewhere in thesystem 100, or multiple historians could be distributed in differentlocations in the system 100.

In particular embodiments, the various controllers and operator stationsin FIG. 1 may represent computing devices. For example, each of thecontrollers could include one or more processing devices 142 and one ormore memories 144 for storing instructions and data used, generated, orcollected by the processing device(s) 142. Each of the controllers couldalso include at least one network interface 146, such as one or moreEthernet interfaces or wireless transceivers. Also, each of the operatorstations could include one or more processing devices 148 and one ormore memories 150 for storing instructions and data used, generated, orcollected by the processing device(s) 148. Each of the operator stationscould also include at least one network interface 152, such as one ormore Ethernet interfaces or wireless transceivers.

In accordance with this disclosure, various components of the system 100support a process for providing cost of lost opportunity to operators inreal time using advance process control in the system 100. For example,one or more of the interfaces 146, 152 could indicate different aspectsof a system where the process controls are not operating with maximumefficiency, as described in greater detail below.

Although FIG. 1 illustrates one example of an industrial process controland automation system 100, various changes may be made to FIG. 1. Forexample, a control system could include any number of sensors,actuators, controllers, servers, operator stations, and networks. Also,the makeup and arrangement of the system 100 in FIG. 1 is forillustration only. Components could be added, omitted, combined, orplaced in any other suitable configuration according to particularneeds. Further, particular functions have been described as beingperformed by particular components of the system 100. This is forillustration only. In general, process control systems are highlyconfigurable and can be configured in any suitable manner according toparticular needs.

FIGS. 2A and 2B illustrate an exemplary interface 200 for providing costof lost opportunity to operators in real time using advance processcontrol according to the various embodiments of the present disclosure.The embodiments of the interfaces illustrated in FIGS. 2A and 2B are forillustration only. FIGS. 2A and 2B do not limit the scope of thisdisclosure to any particular implementation.

The interface 200 includes a table 205 and a visual representation 210.The interface 200 provides a real-time understanding of the field deviceor system being monitored for lost opportunity costs. The interface 200can further provide the ability to adjust the field device or systemaccording to the cost of lost opportunity.

The table 205 includes a CV number 215, a CV name 220, a CV description225, a CV status 230, a CV value 235, a CV SS (steady state) value 240,a CV low limit 245, and a CV high limit 250, for examples among othervariables. The table 205 allows the user to both view the current statusand values for the CVs and also to adjust values in both predictivemodeling and actual operating adjustments.

The CV number 215 is a number associated with a CV for purposes ofordering and linking to a storage. The CV name 220 is the part number orfile link associated with the CV in the interface and the CV description225 provides a semantic name to the CV for identification by the user.The CV status 230 provides the user with the operating status of eachCV. The CV value 235 is the current operating value of the CV. The CV SSvalue 240 is the CV steady state value. The CV low limit 245 is the lowend of a range of acceptable current values for the CV. The CV highlimit 250 is the high end of the range of acceptable current values forthe CV.

The visual representation 210 includes one or more charts, such as a barchart 255 and a doughnut chart 260. The visual representation 210 of theinterface 200 provides an easily discernable representation of the totalamount of lost opportunity costs and the significance of each type oflost opportunity costs. The visual representation also adjusts inreal-time to indicate changes in the lost opportunity costs. The chartsillustrated in the visual representation of FIG. 2A are percentages ofthe lost opportunity costs, but could also be represented using actualloss values.

The visual representation 210 could also include pop-up windows 265 thatinclude more detailed information about each lost opportunity cost. Thepop-up window 265 could be generated when the user selects a specificcost opportunity in the table 205 or by selecting or hovering over aspecific portion of a chart.

Operators are responsible to use APC effectively to drive the processunit to the optimal operating targets. There is no standard mechanism tomeasure the performance of the process unit and how far the performanceis from the optimal operation of the process unit. Operators need toknow the answers to the following questions, as examples of questions,in real time to take corrective actions to drive their process units tooptimal operating targets:

Is my process unit running at optimal operating conditions? Whichvariables are causing sub-optimal performance of my process unit andwhy? What should I do to fix the loss of performance of my process unit?

Process units sometimes undergo temporary changes due to preventivemaintenance activities such as providing bypass while a valve is beingrepaired. Operators make changes in the APC application such as a limitchange or drop a variable from the APC to account for these processchanges. These temporary changes in the APC application are not revertedback to optimal operating targets due to lack of communication duringshift handovers or otherwise. This limits maneuverability of variablesin the APC application resulting in sub-optimal performance.

Thus, there is a need to measure performance of the process unit andprovide corrective actions to operators in real time.

A process for determining cost of lost opportunity is executed atregular intervals. It identifies various APC issues and provides theeconomic value of loss. It also provides the corrective action toaddress the issue. This information is provided to operators in realtime.

If a temporary change in the APC application configuration is notreverted back to the optimal configuration, then the cost of lostopportunity algorithm can flag it as an issue and provide the economicvalue of the loss associated with the issue. This drives operators totake corrective actions irrespective of which shift the operator belongsto.

This solution is vendor neutral and hence can be applied in any processunit where some vendor APC is running.

The method to calculate the cost of lost opportunity is to run a shadowoptimizer at regular intervals. The data which is required for runningthe optimizer would be largely the same data as the one which isrequired for the advanced controller. In addition to the advancedcontroller data and gain matrix, it would require ideal limits of thevariables. Ideal limits can be sourced from boundary management softwarewhich defines the operating envelope. Ideal limits can also be sourcedfrom initial benefit study used to justify APC.

A sample screenshot of interface 200 is shown highlighting visualizationof cost of lost opportunity of variables in an APC controller. Thedoughnut chart 260 shows how much economic loss is associated withdifferent types of issues such as limit issues, variables droppedissues, etc. The bar chart 255 lists the top five variables that causesub-optimal performance of an APC controller and how to fix it.

Economic optimization (EO) (also referred to as the optimizer oreconomic optimizer), which is solved in an APC, can be obtained bysatisfying the following equation.

${E\; O} = {\min \; \frac{1}{2}{\phi }_{2}^{2}}$ whereϕ = ∑_(i)α_(i) * (y_(i) − y_(i o))² + β_(i) * y_(i) + ∑_(j)α_(j) * (u_(j) − u_(j o))² + β_(j) * u_(j)subject   to:   y_(l i) ≤ y_(i) ≤ y_(h i)u_(l j) ≤ u_(j) ≤ u_(h j)

The method to calculate the cost of lost opportunity is to run a shadowoptimizer at regular intervals. The optimizer can be run on premise orin the cloud. The data that is required for running the shadow optimizerwould be largely the same data as the one that is required for theadvanced controller. In addition to the advanced controller data andgain matrix, the following data would be required:

Prices of feeds, products, raw materials and utilities at the boundaryof the plant. Prices can be sourced from the ERP.

Ideal limits of the variables. Ideal limits can also be sourced fromboundary management software that defines the operating envelope. Theideal limits are sourced from the initial benefit study used to justifythe APC.

The shadow optimizer is set up as a product value optimizer (PVO). PVOrefers to optimization when true process economics are directly enteredinto the controller in either independent variables or dependentvariables. The shadow optimizer may need to be augmented with additionalcontrolled variables if the original controller is not designed as aPVO.

FIG. 3 illustrates an example method for providing cost of lostopportunity to operators in real time using advance process controlaccording to this disclosure. The process depicted in FIG. 3 isdescribed as being performed in conjunction with the controller 106,processing device 142, or processing device 148 illustrated in FIG. 1.

In operation 305, the controller 106 receives current values and averagevalues for controlled variables and manipulated variables of the fielddevice. The controller 106 can control different sensors to performreadings of the controlled variables or processing devices 142 and 148can receive the values from the field device.

In operation 310, the controller 106 determines a cost of lostopportunity for controlled variable variance issues. This cost of lostopportunity is a portion of a calculation of cost of lost opportunitydue to limit issues. This operation also can b e used calculate the costof loss, if any, due to wrong configuration of the optimizer in theadvance process controller and also calculate the cost of lostopportunity due to noise and variability in controlled variables.

The controller 106 determines an operating object function value basedon real prices and data and limits collected from the field device. Thecontroller 106 determines a steady state object function value based onadjusting the controlled variables in a manner that an average value isequal to a steady state value for each of the controlled variables. Thecontroller 106 determines the cost of lost opportunity for controlledvariable variance issues based on a difference between the steady stateobject function value and the operating object function value.

The cost of lost opportunity can be calculated using the shadowoptimizer as described above. The controller values can be read in theshadow optimizer. The values can be hourly average values or collectedat higher or lower frequency with or without the averaging.

The current optimizer object function value or operating object functionvalue, φ_(i) is determined with the real prices and the data and limitscollected from the plant. In certain embodiments, a difference in steadystate (SS) values of controlled variables and average values ofcontrolled variables can vary. Large differences between the two valuescan indicate loss of opportunity due to variance issues.

The average value of each CV is changed to the SS value and the steadystate objective function value, φ_(s), is recalculated after eachchange.

The difference between φ_(i) and φ_(s) of the first CV indicates theloss of opportunity due to that CV and subsequent change in φ_(s) canindicate loss of opportunity due to variance in a respective CV.

In operation 315, the controller 106 determines a cost of lostopportunity for limit issues. The controller 106 determines an economicobject function value subject to a controlled or manipulated variablerange limit. The controller 106 determines a limit issues objectfunction value by perturbing each end of the controlled or manipulatedvariable range limit for each variable. The controller 106 determinesthe cost of lost opportunity for limit issues based on a differencebetween the limit issues object function value and the economic objectfunction value.

Running the optimizer reports the new objective function value, φ_(o).The difference between final φ_(s) and φ_(o) is the cost due tooptimization configuration issue.

The controller 106 perturbs the limits in constraint to the ideal limitand solve the optimizer to calculate the optimized objective functionvalue, φ_(k), at each perturbation. The difference between φ_(k) andφ_(o) is the loss due to the kth limit. Revert the limit back to theoriginal value after each perturbation.

The controller 106 can store the result of loss due to optimizerconfiguration issue and loss due to the kth limit against the name ofthe variable. Store the result of loss due to limit analysis of eachvariable against the name of the variable.

In operation 320, the controller 106 determines a cost of lostopportunity due to model quality issues. The controller 106 determinesan economic objection function value subject to a controlled andmanipulated variable range limits. The controller 106 determines theactual gains in the field device. The controller 106 determines a modelquality object function value based on the actual gains. The controller106 determines the cost of lost opportunity for model quality issuesbased on a difference between the model quality object function valueand the economic object function value.

Model quality issue can be quantified by first determining the modelerror. The method described in Apparatus and method for analyzing modelquality in a process control environment (U.S. Pat. No. 7,421,374),which is hereby incorporated by reference, can be used for determiningthe actual gains in the advanced controller.

The gains or gain multipliers thus obtained are inserted in the shadowoptimizer model matrix.

The shadow optimizer is solved to determine the optimized objectivefunction value or model quality object function value, φ_(m), when themodel quality is correct. During this optimization run the limits arekept as read from the plant so that quantification is independent of thelimit issues to avoid double dipping.

The difference between φ_(m), and φ_(o) would be cost of lostopportunity due to model quality and is recorded by the controller 106in operation 335.

In operation 325, the controller 106 determines a cost of lostopportunity due to inferential quality issues. The controller 106receives predictive values for inferential sensors. The controller 106measures the actual values for the inferential sensors. The controller106 determines a bias of the inferential sensors based on an average anda standard deviation between the actual values and the predictivevalues.

Inferential or soft sensors can have issues in multiple ways.Inferential or soft sensors can have high variance or high bias. In someembodiments, inferential or soft sensors can have both high variance andbias.

Most inferential sensors are provided with a lab update or update froman analyzer. When an update does not occur, there is no way to figureout the issue with an inferential sensor.

If the inferential sensor generated is not noisy nor with highvariability of bias between lab and predicted value, successive biasupdates can gradually reduce the offset.

If the inferential sensor generated has high variability of bias, theresult is high CV variability. High bias variability can be due toprediction variability or lab variability.

The controller 106 calculates the difference between lab value andpredicted value. For example, more than 10 days of readings are used toeffectively calculate the statistics.

The controller 106 calculates the average, standard deviation and otherdescriptive statistics of the bias.

With a better quality of inferential model, the control variables canoperate closer to the limit. The average shift possible is calculated byextending the method defined by Martin and Turpin in Martin, G., L.Turpin, and R. Cline, Estimating Control Function Benefits, HydrocarbonProcessing, 68-73. 1991, the entirety of which is hereby incorporated byreference.

The controlled variable is corrected for the average shift and theobjective function calculated in the shadow optimizer to estimate theloss due to inferential quality issue.

In operation 330, the controller 106 determines a cost of lostopportunity due to variable mode issues. The controller 106 detects amanipulated variable dropped from consideration. The controller 106measures a current value of the dropped manipulated variable. Thecontroller 106 determines an operating object function value based onreal prices and data and limits collected from the field device. Thecontroller 106 determines a dropped variable object function value basedon accounting for the current value of the dropped controlled variable.The controller 106 determines the cost of lost opportunity based on adifference between the dropped variable object function value and theoperating object function value.

As explained above, one of the reasons why advance process controllerperformance starts to deteriorate is when the manipulated or controlledvariables that should be part of model matrix are dropped and do notparticipate in the optimization problem. Solving the problem in ageneric way requires knowledge of the mode of operation and the variablestatus in each mode because some of the variables are designed to bedropped in certain modes of operation.

Collecting the mode of operation and setting the variable statusproperly in the shadow optimizer, the economic optimizer is solved tocalculate the loss due to variable mode issues.

In operation 335, the controller 106 stores the costs of lostopportunities for the field device. The controller 106 compares each ofthe lost opportunities and presents on a display the information tousers, such as a process control engineer and an operations manager,such that they can make effective decisions.

In certain embodiments, the controller 106 uses the costs of lostopportunities to adjust the operation of the field device automatically.The controller 106 determines adjusted operating values to decrease thecosts of lost opportunities. The controller 106 then operates the fielddevice using the adjusted values.

Although FIG. 3 illustrates one example of a method 300 for providingcost of lost opportunity to operators in real time using advance processcontrol, various changes may be made to FIG. 3. For example, varioussteps shown in FIG. 3 could overlap, occur in parallel, occur in adifferent order, or occur any number of times.

It may be advantageous to set forth definitions of certain words andphrases used throughout this patent document. The terms “transmit,”“receive,” and “communicate,” as well as derivatives thereof,encompasses both direct and indirect communication. The terms “include”and “comprise,” as well as derivatives thereof, mean inclusion withoutlimitation. The term “or” is inclusive, meaning and/or. The phrase“associated with,” as well as derivatives thereof, may mean to include,be included within, interconnect with, contain, be contained within,connect to or with, couple to or with, be communicable with, cooperatewith, interleave, juxtapose, be proximate to, be bound to or with, have,have a property of, have a relationship to or with, or the like. Thephrase “at least one of,” when used with a list of items, means thatdifferent combinations of one or more of the listed items may be used,and only one item in the list may be needed. For example, “at least oneof: A, B, and C” includes any of the following combinations: A, B, C, Aand B, A and C, B and C, and A and B and C.

While this disclosure has described certain embodiments and generallyassociated methods, alterations and permutations of these embodimentsand methods will be apparent to those skilled in the art. Accordingly,the above description of example embodiments does not define orconstrain this disclosure. Other changes, substitutions, and alterationsare also possible without departing from the spirit and scope of thisdisclosure, as defined by the following claims.

What is claimed is:
 1. A field device comprising: a memory; a processor operably connected to the memory; the processor configured to: receive current values and average values for controlled variables and manipulated variables, determine costs of lost opportunity for each of controlled variable variance issues, limit issues, model quality issues, inferential quality issues, and variable model issues based on the current values and the average values of the controlled variables, and store the costs of lost opportunity for the field device.
 2. The field device of claim 1, wherein the processor is further configured to: determine adjusted operating values to decrease the costs of lost opportunity; and operate the field device using the adjusted values.
 3. The field device of claim 1, wherein determining a cost of lost opportunity for controlled variable variance issues comprises: determine an operating object function value based on real prices and data and limits collected from the field device; determine a steady state object function value based on adjusting the controlled variables in a manner that an average value is equal to a steady state value for each of the controlled variables; and determine the cost of lost opportunity for controlled variable variance issues based on a difference between the steady state object function value and the operating object function value.
 4. The field device of claim 1, wherein determining a cost of lost opportunity for limit issues comprises: determine an economic object function value subject to a controlled variable range limit or a manipulated variable range limit; determine a limit issues object function value by perturbing each end of the controlled variable range limit or the manipulated variable range limit for each variable; and determine the cost of lost opportunity for limit issues based on a difference between the limit issues object function value and the economic object function value.
 5. The field device of claim 1, wherein determining a cost of lost opportunity for model quality issues comprises: determine an economic object function value subject to a controlled variable range limit and a manipulated variable range limit; determine actual gains in the field device; determine a model quality object function value based on the actual gains; and determine the cost of lost opportunity for model quality issues based on a difference between the model quality object function value and the economic object function value.
 6. The field device of claim 1, wherein determining a cost of lost opportunity for inferential quality issues comprises: receive predictive values for an inferential sensor; measure actual values for the inferential sensor; determine a bias of the inferential sensor based on an average and a standard deviation of a difference between the actual values and the predictive values; and determine the cost of lost opportunity for inferential sensor quality issues based on a shift of controlled variable associated with the inferential sensor to reduce the difference between the actual values and the predictive values.
 7. The field device of claim 1, wherein determining a cost of lost opportunity for variable model issues comprises: detect a manipulated variable dropped from consideration; measure a current value of the dropped manipulated variable; determine an operating object function value based on real prices and data and limits collected from the field device; determine a dropped variable object function value based on accounting for the current value of the dropped manipulated variable; and determine the cost of lost opportunity for dropped variables based on a difference between the dropped variable object function value and the operating object function value.
 8. A method for a field device comprising: receiving current values and average values for controlled variables and manipulated variables; determining costs of lost opportunity for each of controlled variable variance issues, limit issues, model quality issues, inferential quality issues, and variable model issues based on the current values and the average values of the controlled variables; and storing the costs of lost opportunity for the field device.
 9. The method of claim 8, further comprising: determining adjusted operating values to decrease the costs of lost opportunity; and operating the field device using the adjusted values.
 10. The method of claim 8, wherein determining a cost of lost opportunity for controlled variable variance issues comprises: determining an operating object function value based on real prices and data and limits collected from the field device; determining a steady state object function value based on adjusting the controlled variables in a manner that an average value is equal to a steady state value for each of the controlled variables; and determining the cost of lost opportunity for controlled variable variance issues based on a difference between the steady state object function value and the operating object function value.
 11. The method of claim 8, wherein determining a cost of lost opportunity for limit issues comprises: determining an economic object function value subject to a controlled variable range limit or a manipulated variable range limit; determining a limit issues object function value by perturbing each end of the controlled variable range limit or the manipulated variable range limit for each variable; and determining the cost of lost opportunity for limit issues based on a difference between the limit issues object function value and the economic object function value.
 12. The method of claim 8, wherein determining a cost of lost opportunity for model quality issues comprises: determining an economic object function value subject to a controlled variable range limit and a manipulated variable range limit; determining actual gains in the field device; determining a model quality object function value based on the actual gains; and determining the cost of lost opportunity for model quality issues based on a difference between the model quality object function value and the economic object function value.
 13. The method of claim 8, wherein determining a cost of lost opportunity for inferential quality issues comprises: receiving predictive values for an inferential sensor; measuring actual values for the inferential sensor; determining a bias of the inferential sensor based on an average and a standard deviation of a difference between the actual values and the predictive values; and determining the cost of lost opportunity for inferential sensor quality issues based on a shift of controlled variable associated with the inferential sensor to reduce the difference between the actual values and the predictive values.
 14. The method of claim 8, wherein determining a cost of lost opportunity for variable model issues comprises: detecting a manipulated variable dropped from consideration; measuring a current value of the dropped manipulated variable; determining an operating object function value based on real prices and data and limits collected from the field device; determining a dropped variable object function value based on accounting for the current value of the dropped controlled variable; and determining the cost of lost opportunity for dropped variables based on a difference between the dropped variable object function value and the operating object function value.
 15. A non-transitory machine-readable medium encoded with executable instructions that, when executed, cause one or more processors to: receive current values and average values for controlled variables and manipulated variables; determine costs of lost opportunity for each of controlled variable variance issues, limit issues, model quality issues, inferential quality issues, and variable model issues based on the current values and the average values of the controlled variables; and store the costs of lost opportunity for a field device.
 16. The non-transitory machine-readable medium of claim 15, wherein determining a cost of lost opportunity for controlled variable variance issues comprises: determine an operating object function value based on real prices and data and limits collected from the field device; determine a steady state object function value based on adjusting the controlled variables in a manner that an average value is equal to a steady state value for each of the controlled variables; and determine the cost of lost opportunity for controlled variable variance issues based on a difference between the steady state object function value and the operating object function value.
 17. The non-transitory machine-readable medium of claim 15, wherein determining a cost of lost opportunity for limit issues comprises: determine an economic object function value subject to a controlled variable range limit or a manipulated range limit; determine a limit issues object function value by perturbing each end of the controlled variable range limit or the manipulated variable range limit for each variable; and determine the cost of lost opportunity for limit issues based on a difference between the limit issues object function value and the economic object function value.
 18. The non-transitory machine-readable medium of claim 15, wherein determining a cost of lost opportunity for model quality issues comprises: determine an economic object function value subject to a controlled variable range limit and a manipulated variable range limit; determine actual gains in the field device; determine a model quality object function value based on the actual gains; and determine the cost of lost opportunity for model quality issues based on a difference between the model quality object function value and the economic object function value.
 19. The non-transitory machine-readable medium of claim 15, wherein determining a cost of lost opportunity for inferential quality issues comprises: receive predictive values for an inferential sensor; measure actual values for the inferential sensor; determine a bias of the inferential sensor based on an average and a standard deviation of a difference between the actual values and the predictive values; and determine the cost of lost opportunity for inferential sensor quality issues based on a shift of controlled variable associated with the inferential sensor to reduce the difference between the actual values and the predictive values.
 20. The non-transitory machine-readable medium of claim 15, wherein determining a cost of lost opportunity for variable model issues comprises: detect a manipulated variable dropped from consideration; measure a current value of the dropped manipulated variable; determine an operating object function value based on real prices and data and limits collected from the field device; determine a dropped variable object function value based on accounting for the current value of the dropped manipulated variable; and determine the cost of lost opportunity for dropped variables based on a difference between the dropped variable object function value and the operating object function value. 